I am working on my dataset and quite new to this. Below is the code:
class_col_name='Creditability'
feature_names=df.columns[df.columns != class_col_name ]
# 70% training and 30% test
X_train, X_test, y_train, y_test = train_test_split(df.loc[:, feature_names], df[class_col_name], test_size=0.3,random_state=1)
print("Number transactions X_train dataset: ", X_train.shape)
print("Number transactions y_train dataset: ", y_train.shape)
print("Number transactions X_test dataset: ", X_test.shape)
print("Number transactions y_test dataset: ", y_test.shape)
print("Before OverSampling, counts of label '1': {}".format(sum(y_train == 1)))
print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train == 0)))
I am trying to apply oversampling on my dataset, but when I count it before oversampling it says 0 in the output but it do show me that dataset has data:
Below is the output:
Number transactions X_train dataset: (700, 20)
Number transactions y_train dataset: (700,)
Number transactions X_test dataset: (300, 20)
Number transactions y_test dataset: (300,)
Before OverSampling, counts of label '1': 0
Before OverSampling, counts of label '0': 0
I am trying to understand the output and work on it.
You might want to confirm that the possible class labels are in fact 0 and 1. You could try
print(y_train.unique())
to check what the class labels are.
If y_train is a pandas Series with labels in [0, 1], then I believe the results of the last two lines should in fact sum to the size of y_train. If the labels are not in the integers 0 or 1 then that would explain why the sums are both 0.